Triple
T37790675
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lucie de Mirecourt |
E942076
|
entity |
| Predicate | hasPortrayedInMedium |
P177716
|
FINISHED |
| Object | live-action film |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: live-action film | Statement: [Lucie de Mirecourt, hasPortrayedInMedium, live-action film]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPortrayedInMedium Context triple: [Lucie de Mirecourt, hasPortrayedInMedium, live-action film]
-
A.
portrayedInFilmMedium
Indicates that an entity is depicted or represented within a film or cinematic work.
-
B.
isPortrayedIn
chosen
Indicates that an entity is depicted or represented within a particular work, medium, or portrayal.
-
C.
hasFilmographyIn
Indicates that an individual has participated in or contributed to works within a specified filmography or body of film-related productions.
-
D.
hasPortrayedRole
Indicates that an entity has performed or depicted a specific role or character, typically in a work such as a film, play, or television show.
-
E.
hasYoungPortrayalOf
Indicates that one entity is a portrayal or depiction of another entity specifically in their younger age or earlier life stage.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f76ee5cb0c81909a363d1c929156c0 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69ffdd05d1908190957deb11392f4595 |
completed | May 10, 2026, 1:19 a.m. |
| PD | Predicate disambiguation | batch_69ffdc0d33c881908b3483bee8a96540 |
completed | May 10, 2026, 1:14 a.m. |
Created at: May 3, 2026, 4:19 p.m.